Super-resolution is also known as up-sampling and image magnification, which refers to recovering a high-resolution image from a low-resolution image. Super-resolution is one of the basic operations in image and video processing, with wide application prospect in the fields of medical image processing, image recognition, digital photo processing, high-definition television, etc.
One of the most common super-resolution algorithms is kernel-based interpolation algorithm, such as bilinear interpolation, spline interpolation, and so on. However, the interpolation algorithm generates continuous data by using known discrete data, blurring, jagging, and other problems may occur and the effect of image restoration is not good.
In recent years, a large number of image edge-based super-resolution algorithms have been put forward to improve the unnatural effects of images reconstructed by using traditional interpolation algorithms and improve the visual quality of edge of reconstructed images. However, this type of algorithm focuses on improving the edges and cannot recover high-frequency texture details. To solve the problem of high-frequency detail reconstruction, some dictionary learning methods have been put forward, which use the existing high-resolution image blocks to train the high-resolution dictionary corresponding to the low resolution, and then use the high-resolution dictionary to recover the lost details in the low-resolution image. However, in the traditional methods that use dictionaries to recover high-resolution images, the accuracy of matching affects the quality and the effect of the image reconstruction because the dictionary needs to be matched with the low-resolution images. Therefore, improving matching accuracy and the reconstruction quality of low-resolution images becomes a key research direction in the field of image processing.